Validating clusters using the Hopkins statistic

Amit Banerjee, Rajesh N. Davé

Research output: Chapter in Book/Report/Conference proceedingConference contribution

174 Scopus citations


A novel scheme for cluster validity using a test for random position hypothesis is proposed. The random position hypothesis is tested against an alternative clustered hypothesis on every cluster produced by a partitioning algorithm. A test statistic such as the well-known Hopkins statistic could be used as a basis to accept or reject the random position hypothesis, which is also the null hypothesis in this case. The Hopkins statistic is known to be a fair estimator of randomness in a data set. The concept is borrowed from the clustering tendency domain and its applicability to validating clusters is shown here using two artificially constructed test data sets.

Original languageEnglish (US)
Title of host publication2004 IEEE International Conference on Fuzzy Systems - Proceedings
Number of pages5
StatePublished - 2004
Event2004 IEEE International Conference on Fuzzy Systems - Proceedings - Budapest, Hungary
Duration: Jul 25 2004Jul 29 2004

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584


Other2004 IEEE International Conference on Fuzzy Systems - Proceedings

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics


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